Background

Tip

The RD-CDM paper has now been published at Nature Scientific Data. You can read it here!

Introduction

Rare diseases (RDs), though individually rare, collectively impact over 260 million people worldwide, with over 17 million affected in Europe. These conditions, defined by their low prevalence of fewer than 5 in 10,000 individuals, are often genetically driven, with over 70% of cases suspected to have a genetic cause. Despite significant advances in medical research, RD patients still face lengthy diagnostic delays, often due to a lack of awareness in general healthcare settings and the rarity of RD-specific knowledge among clinicians. Misdiagnosis and underrepresentation in routine care further compound the challenges, leaving many patients without timely and accurate diagnoses.

Interoperability plays a critical role in addressing these challenges, ensuring the seamless exchange and interpretation of medical data through the use of internationally agreed standards. In the field of rare diseases, where data is often scarce and scattered, the importance of structured, standardized, and reusable medical records cannot be overstated. Interoperable data formats allow for more efficient research, better care coordination, and a clearer understanding of complex clinical cases. However, existing medical systems often fail to support the depth of phenotypic and genotypic data required for rare disease research and treatment, making interoperability key for improving outcomes in RD care.

To address these needs, we introduce our RD-CDM v2.0.0 — a common data model specifically designed for rare diseases. This RD-CDM simplifies the capture, storage, and exchange of complex clinical data, enabling researchers and healthcare providers to work with harmonized datasets across different institutions and countries. The RD-CDM is based on the ERDRI-CDS, a common data set developed by the European Rare Disease Research Infrastructure (ERDRI) to support the collection of harmonized data for rare disease research. By extending the ERDRI-CDS with additional concepts and relationships, based on HL7 FHIR v4.0.1 and the GA4GH Phenopacket Schema v2.0, the RD-CDM provides a comprehensive model for capturing detailed clinical information alongisde precise genetic data on rare diseases.

Methodology

Steps in the development of the ontology-based Rare Disease Common Data Model (RD-CDM) harmonising international registry use, FHIR and Phenopackets.As many steps were performed concurrently and overlapped across multiple sites, this methodology should be considered a non-hierarchical approach. First, we included and assessed previous RD data models, followed by mapping elements to FHIR basic resources v4.0.1 and Phenopacket Schema v2.0 elements. A clinical evaluation was performed to assess the relevance of these elements while balancing the data model’s scope and spectrum of data granularity. We then performed ontology-based encoding to establish a common denominator between the models and data standards. Prototypical versions of our RD-CDM were implemented in REDCap, capturing real patient data from various RDs and use cases. Additionally, the project was developed in our public ART-DECOR project, and open-source GitHub repository alongside its documentation to ensure sustainability, reusability and flexibility for future improvements and usage.

Overview

Overview of the RD-CDM v2.0.0 showing the data elements and sections. The RD-CDM does not define cardinalities or relationships to allow for nation-specific balloting and implementation.

Note

The RD-CDM is a community-driven project, and we welcome contributions from researchers, clinicians, and other stakeholders in the rare disease community. If you would like to contribute to the RD-CDM, please read our contributing guidelines.

Table Columns

This Figure Provides an overview of the table columns used to depict our Rare Disease Common Data Model (RD-CDM). Each column’s abbreviation, further definitions, and explanations are given. We recommend referring to this figure when reading the tables for each section of our RD-CDM.

Note

The table can be found in Figshare at the following link: RD-CDM v2.0.0 Excel Table.

or can be downloaded here: RD-CDM v2.0.0 Excel Table.

Layers of harmonisation

We analysed to what extent interoperability requirements were met while harmonising data elements from the ERDRI-CDS, HL7 FHIR resources and the GA4GH Phenopacket Schema to a single RD-CDM. We identified six layers of harmonisation on the level of each data element: (1) the Alignment Layer, (2) the Labelling Layer, (3) the Terminology Binding Layer, (4) the Data Type Layer, (5) the Value Set Layer, and (6) the Value Set Choice Layer. All layers and their selection criteria are depicted in the figure below.

While over 95% of all data elements are directly aligned with HL7 FHIR or GA4GH Phenopackets, only one-third of terminology bindings and 85% of value types match the specifications outlined by these standards. More than 87% of value sets being directly are aligned with the specifications defined by HL7 FHIR and GA4GH Phenopacket Schema,

Attention

The RD-CDM paper is currently under review. As soon as it is published, we will provide a link to the paper here and all tables and figures will be available in the paper.